Mobile cardiac monitoring device

Abstract

The invention title of the present disclosure is "Mobile Heart Monitoring Device". A mobile heart monitoring device is disclosed. The mobile heart monitoring device receives voltage-time measurements of a subset of the user's electrocardiogram (ECG) leads and derives a complete set of ECG leads from the voltage-time measurements of the subset of the ECG leads. The mobile heart monitoring device calculates a heart rate based on at least one of the subset of the ECG leads and monitors the user's heart rhythm, and calculates cardiac electrobiomarkers (CEBs) based on the derived ECG. The mobile heart device detects a trigger condition based on the calculated CEBs and transmits an alert in response to detecting the trigger condition.

Description

Field of the Invention

[0001] The described invention relates to mobile cardiac monitoring devices and, more particularly, to mobile devices for monitoring heart rhythm and dynamic cardiac electrobiomarkers. Background of the Invention

[0002] Coronary heart disease, which causes acute coronary syndrome (ACS), is the leading cause of mortality in the United States, and chest pain accounts for more than 8 million emergency room visits each year. However, acute myocardial infarction (AMI) is often misdiagnosed in the emergency room, and many patients with AMI are allowed to leave the emergency room without being identified.

[0003] Cardiac Electrophysiology

[0004] Transmembrane ionic currents are substantially responsible for the cardiac potentials recorded as the ECG. The ECG is the end result of a series of complex physiological and technical processes. Transmembrane ionic currents are generated by the flow of ions across cell membranes and between adjacent cells. These currents are synthesized through the cardiac activation and recovery sequences to generate the cardiac electric field in and around the heart, which varies over time during the cardiac cycle. This electric field passes through many other structures, including the lungs, blood, and skeletal muscle, which interfere with the cardiac electric field as it passes through them. Braunwald's Heart Disease, 8th Ed., Saunders, Elsevier (2008), Chapter 12, at p. 149. The currents that reach the skin are then detected by electrodes placed at specific locations on the limbs and torso, which are configured to produce leads. The outputs of these leads are amplified, filtered, and displayed by various electronic devices to produce an electrocardiogram recording, and diagnostic criteria are applied to these recordings to produce an interpretation.

[0005] Cardiac Dipole

[0006] Two point sources of equal strength but opposite polarity that are located very close to each other, such as a current source and a current sink, can be represented as a current dipole. Thus, the activation of a single cardiac fiber can be modeled as a current dipole that moves in the direction of propagation of the activation. This dipole is completely characterized by three parameters: strength or dipole moment, position, and orientation. The dipole moment is proportional to the rate of change of the intracellular potential. Similarly, multiple adjacent cardiac fibers are activated synchronously to produce an activation front, which creates a dipole oriented in the direction of activation. The net effect of all the dipoles in this wave front is a single dipole with an intensity and orientation equal to the (vector) sum of all the simultaneously active component dipoles.

[0007] An electric current dipole generates a characteristic potential field that has a positive potential projected in front of it and a negative potential projected behind it. The actual potential recorded anywhere within this field is proportional to the dipole moment, inversely proportional to the square of the distance from the dipole to the recording location, and proportional to the cosine of the angle between the axis of the dipole and the line drawn from the dipole to the recording location.

[0008] This relationship between the activation direction, the orientation of the electric current dipole, and the polarity of the potential describes the fundamental relationship between the polarity of the potential sensed by the electrode and the direction of movement of the activation front, that is, the electrode senses a positive potential when the activation front is moving towards it and a negative potential when the activation front is moving away from it.

[0009] The transmission factors are the contents of the three-dimensional physical environment (referred to as the volume conductor), which modify the cardiac electric field in a significant way. The transmission factors can be grouped into four general categories.

[0010] Cell factors determine the intensity of the current flux (which results from the local transmembrane potential gradient); they include the intracellular and extracellular resistances and the concentrations of relevant ions (such as sodium ions). Lower ion concentrations reduce the intensity of the current and lower the extracellular potential.

[0011] Cardiac factors affect the relationship of one cardiac cell to another. Two main factors are: (1) anisotropy, a property of cardiac tissue that causes greater current and faster propagation along the length of the fibers compared to across the width of the fibers; and (2) the presence of connective tissue between cardiac fibers, which interrupts the effective electrical coupling of adjacent fibers.

[0012] Extra-cardiac factors encompass all the tissues and structures located between the activation region and the body surface, including the ventricular wall, the blood within the heart and the thorax, the pericardium, the lungs, the skeletal muscle, the subcutaneous fat, and the skin. These tissues alter the cardiac field due to differences in the resistivity of adjacent tissues (i.e., the presence of electrical inhomogeneities within the torso).

[0013] Other factors include changes in the distance between the heart and the recording electrode, which proportionally reduce the potential magnitude with the eccentricity of the heart within the thorax and the square of the distance (meaning the heart is located closer to the anterior region of the torso rather than the posterior region, and the anterior septum of the right and left ventricles is located closer to the posterior thoracic wall compared to the other parts of the left ventricle and atria, which means the electrocardiogram potential will be higher in the anteroposterior than in the posterior, and the waveforms projected from the anterior left ventricle to the thoracic wall will be larger than those generated by the posterior ventricular region).

[0014] Cardiac cycle

[0015] The heart is an electric current generator, and its electric field is well-known to be overwhelmingly dipole.

[0016] The term "cardiac cycle" is used to refer to all or any of the electrical and mechanical events associated with coronary blood flow or blood pressure that occur from the start of one heartbeat to the start of the next. Blood pressure rises and falls throughout the cardiac cycle. The frequency of the cardiac cycle is the heart rate. Each single 'beat' of the heart involves five main stages: (1) "late diastole", which is the time when the semilunar valves close, the atrioventricular (AV) valves open, and the whole heart relaxes; (2) "atrial systole", which is the time when the left and right atria are contracting, the AV valves are open, and blood is flowing from the atria into the ventricles; (3) "isovolumic ventricular contraction", which is the time when the ventricles start to contract, the AV and semilunar valves close, and there is no change in volume; (4) "ventricular ejection", which is the time when the ventricles are emptied but still contracting and the semilunar valves are open; and (5) "isovolumic ventricular relaxation", the time when the pressure drops, no blood enters the ventricles, the ventricles stop contracting and start to relax, and the semilunar valves close (because the blood in the large arteries is pushing them closed). The cardiac cycle is coordinated by a series of electrical impulses generated by specialized heart cells present in the sinoatrial node and the atrioventricular node. The heart is activated and restored in a characteristic pattern during each cardiac cycle determined by the anatomy and physiology of the working myocardium and the specialized cardiac conduction system. P. Libby et al., Eds., Braunwald's Heart Disease, 8th Ed., Elsevier, Inc., Philadelphia (2008) at 155.

[0017] The normal cardiac cycle begins with the spontaneous depolarization of the sinoatrial node, a region of specialized tissue located in the high right atrium (RA). The wave of electrical depolarization then passes through the RA and spreads across the interatrial septum into the left atrium (LA).

[0018] The atria are separated from the ventricles by an electrically inert fibrous ring such that in a normal heart, the only route of transmission of electrical depolarization from the atria to the ventricles is through the atrioventricular (AV) node. The AV node delays the electrical signal for a short time, and then the wave of depolarization spreads down the interventricular septum (IVS) via the bundle of His and the right and left bundle branches into the right (RV) and left (LV) ventricles. By normal conduction, the two ventricles contract simultaneously.

[0019] After complete depolarization of the heart, the myocardium must then repolarize before it can be ready to depolarize again for the next cardiac cycle.

[0020] Standard 12-lead electrocardiogram

[0021] A standard surface ECG is recorded, which shows 12 different lead 'directions' from eight independent leads, although only 10 recording electrodes on the skin are required to achieve this. Six of these electrodes are placed on the chest wall covering the heart to record six chest or precordial leads. Four electrodes are placed on the limbs to record six limb leads. In a standard ECG, it is necessary for each of the 10 recording electrodes to be placed in its correct position, otherwise the appearance of the ECG will be significantly altered, preventing correct interpretation.

[0022] For simple bipolar leads such as leads I, II, and III, the lead vector is directed from the negative electrode to the positive electrode. For augmented limb and precordial leads, the origin of the lead vector is at the midpoint of the axis connecting the electrodes (which form a composite electrode), i.e., for lead aVL, the vector points from the midpoint of the axis connecting the right arm and left leg to the left arm. For precordial leads, the lead vector points from the center of the triangle formed by the three standard limb leads to the precordial electrode.

[0023] Limb leads record the ECG in the plane of the coronary arteries and can therefore be used to determine the electrical axis (which is usually only measured in the plane of the coronary arteries). The limb leads are designated as leads I, II, III, aVR, aVL, and aVF. The horizontal line passing through the heart and directed to the left (exactly in the direction of lead I) is conventionally marked as the reference point of 0 degrees (0°). The directions in which the other leads 'view' the heart are described in terms of the angle (in degrees) from this baseline.

[0024] Precordial leads record the ECG in the transverse or horizontal plane and are designated as V1, V2, V3, V4, V5, and V6. Other lead conventions exist and can be used clinically, including V7, V8, and V9 (which record from the left posterior chest) and V3R, V4R, V5R, and V6R (which record from the right anterior chest).

[0025] Improved ECG using a general transformation matrix

[0026] An improved ECG technique for detecting myocardial injury uses a monomorphic optimization algorithm and a mathematical technique of abstract factor analysis to derive a general transformation matrix that is applicable to all patients and is time-independent (U.S. Patent No. 6,901,285, incorporated by reference). This general transformation matrix is applicable when needed and does not require the acquisition of a complete n-lead ECG for each patient prior to its implementation. To do this, one first measures and digitizes the voltage-time data of a certain set of ECG leads to define an ECG training set. Once the voltage-time data array has been acquired, the abstract factor analysis ("AFA") technique is applied to each ECG voltage-time data array in the training set in order to minimize the error in the measured array. The final step then applies the monomorphic optimization technique ("SOP") to the training set in order to derive a general transformation matrix that is applicable to all patients and is time-independent. This general transformation matrix can then be applied to a standard-measured 3-lead subsystem (the measured leads I, aVF, and V2) to derive a standard 12-lead ECG, and can be applied to other systems and is capable of generating at least 22 leads in order to enable a more accurate interpretation of cardiac electrical activity. These derived ECG leads account for approximately 99% of the information content when compared to the measured lead measurements.

[0027] ECG is the first test in the initial evaluation of patients with chest pain, but multiple studies have shown that ECG has low sensitivity in the initial diagnosis of AMI.

[0028] Cardiac serum markers are an important complement to ECG in the evaluation and risk stratification of acute myocardial ischemic injury. Serum troponin evaluation has recently become the gold standard for the diagnosis of myocardial necrosis. However, serum troponin results are generally not immediately available, they are not obtained continuously in real time, and initial treatment protocols usually have to be implemented by relying only on the initial patient evaluation and the associated 12-lead ECG interpretation.

[0029] Rapid diagnosis of acute myocardial ischemic injury (including AMI) is crucial for achieving immediate treatment. For patients suspected of having acute coronary syndrome (ACS), ECG and cardiac serum markers are usually obtained at the time of patient arrival and then every few hours thereafter, up to 24 hours of patient observation, in order to identify the progression of ACS. The patient may be at risk during the time period between these serum marker and ECG acquisitions, especially when the patient has silent ischemic injury. In addition, approximately 95% of patients presenting to the emergency department with chest pain are sent home without treatment. These patients may also be at risk. Summary of the Invention

[0030] The described invention provides a mobile cardiac monitoring device for monitoring a patient's heart rate and rhythm, acquired electrocardiogram (ECG) leads, and cardiac electrobiomarkers. The mobile cardiac monitoring device is capable of deriving a 12-lead ECG to at least a 22-lead ECG (n-lead ECG) from three measured leads, and is capable of calculating dynamic cardiac electrobiomarkers from the derived 12-lead ECG. The mobile cardiac monitoring device is capable of communicating via a data network, such as a cellular network, to transmit an alert when a trigger condition is detected based on the dynamic cardiac electrobiomarkers.

[0031] In one embodiment of the described invention, the mobile cardiac monitoring device receives voltage-time measurements of a subset of the user's ECG leads. A complete set of the user's n-ECG leads is derived from the subset of ECG leads. The heart rate of the user is calculated based on at least one of the measured subset of ECG leads and the rhythm of the user is monitored. Cardiac electrobiomarkers (CEBs) are calculated from the derived complete set of ECG leads.

[0032] In another embodiment of the described invention, the mobile cardiac monitoring device includes: electrocardiogram (ECG) electrodes for acquiring voltage-time measurements of a subset of the user's ECG leads; an ECG derivation module for deriving a complete set of the user's ECG leads from the subset of ECG leads; a heart rate calculation and rhythm monitoring module for calculating the heart rate based on at least one of the measured subset of ECG leads and monitoring the rhythm of the user; and a cardiac electrobiomarker (CEB) calculation module for calculating CEBs from the derived complete set of ECG leads.

[0033] In another embodiment of the described invention, the mobile cardiac monitoring device includes: a processor and a memory storing computer program instructions that, when executed by the processor, cause the processor to perform operations including: deriving a complete set of the user's ECG leads from a subset of ECG leads received from electrocardiogram (ECG) electrodes, calculating the heart rate based on at least one of the measured subset of ECG leads and monitoring the rhythm of the user, and calculating cardiac electrobiomarkers (CEBs) from the derived complete set of ECG leads.

[0034] In another embodiment of the described invention, a system for cardiac monitoring of multiple patients includes a plurality of cardiac monitoring devices and a central monitoring system. Each of the plurality of cardiac monitoring devices obtains voltage-time measurements of a subset of electrocardiogram (ECG) leads of a corresponding one of the multiple patients. Each of the plurality of cardiac monitoring devices transmits the voltage-time measurements of the subset of ECG leads of the corresponding one of the multiple patients via a network. The central monitoring system receives the voltage-time measurements of the subset of ECG leads of each of the multiple patients transmitted from the plurality of cardiac monitoring devices. The central monitoring system derives a corresponding complete set of ECG leads of each of the multiple patients from the corresponding subset of ECG leads. The central monitoring system calculates a corresponding cardiac electrobiomarker (CEB) of each of the multiple patients from the corresponding derived complete set of ECG leads, and detects whether a trigger condition has occurred for each of the multiple patients based on the corresponding CEB calculated for each of the multiple patients.

[0035] These and other advantages of the present invention will be apparent to those skilled in the art by reference to the following detailed description and the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 Illustrates a mobile cardiac monitoring device 100 in accordance with an embodiment of the described invention;

[0037] Figure 2 Illustrates the placement of ECG electrodes on a user's body in accordance with an embodiment of the described invention;

[0038] Figure 3 Illustrates a method for deriving an n-lead ECG in accordance with an embodiment of the described invention;

[0039] Figure 4 Illustrates a typical cardiac electrical signal as measured by an ECG;

[0040] Figure 5 Illustrates a method for cardiac monitoring using a mobile cardiac monitoring device in accordance with an embodiment of the described invention;

[0041] Figure 6 Illustrates a method for cardiac monitoring and alert notification using a mobile cardiac monitoring device in accordance with an embodiment of the described invention;

[0042] Figure 7 Illustrates communication between a mobile cardiac monitoring device 700 and a reader device 710 in accordance with an embodiment of the present invention; and

[0043] Figure 8 Illustrates a system for cardiac monitoring of a patient in accordance with an embodiment of the described invention. Detailed Implementation Modes

[0044] The described invention relates to a mobile cardiac monitoring device. Embodiments of the described invention provide a mobile cardiac monitoring device for monitoring a patient's heart rate and rhythm, acquired electrocardiogram (ECG) leads, and cardiac electrobiomarkers. The mobile cardiac monitoring device can be used to remotely monitor a patient and to monitor the progression of heart disease in real time.

[0045] Figure 1 FIG. 7 shows a mobile cardiac monitoring device 100 according to an embodiment of the described invention. The mobile cardiac monitoring device 100 can be implemented as a stand-alone device or can be implemented as part of another mobile device (such as a cellular phone, tablet, etc.). According to an advantageous embodiment, the cardiac monitoring device 100 is a portable handheld device and can thus be considered a "mobile" or "non-fixed" device. As Figure 1 shown, the mobile cardiac monitoring device 100 includes a processor 102 that is operatively coupled to a data storage device 106 and a memory 104. The processor 102 controls the overall operation of the cardiac monitoring device 100 by executing computer program instructions that define such operations. The computer program instructions can be stored in the data storage device 106 or in a removable storage device 118 and are loaded into the memory 104 when it is desired to execute the computer program instructions. An electrocardiogram (ECG) derivation module 108, a heart rate and rhythm module 110, a dynamic cardiac electrobiomarker (CEB) module 112, and an alarm module 114, as well as the Figure 3 、 Figure 4 and Figure 6 method steps described below can be defined by computer program instructions (which are stored in the data storage device 106) and are controlled by the processor 102 executing the computer program instructions when the computer program instructions are loaded into the memory 104. For example, the computer program instructions can be implemented as computer-executable code programmed by those skilled in the art to perform the Figure 3 、 Figure 4 and Figure 6 method steps and to implement the modules 108, 110, 112, and 114 shown in Figure 1 .

[0046] Processor 102 may include both general-purpose microprocessors and special-purpose microprocessors, and may be one of a single processor or multiple processors of the heart monitoring device 100. Processor 102 may include, for example, one or more central processing units (CPUs). The processor may also include one or more graphics processing units (GPUs). Processor 102, data storage device 106, and / or memory 104 may include one or more application-specific integrated circuits (ASICs) and / or one or more field-programmable gate arrays (FPGAs), by which to supplement, or be incorporated therein.

[0047] Data storage device 106 and memory 104 each include a tangible non-transitory computer-readable storage medium. Memory 104 may include high-speed random access memory, such as dynamic random access memory (DRAM), static random access memory (SRAM), double data rate synchronous dynamic random access memory (DDR RAM), or other random access solid-state memory devices. Data storage device 106 may include non-volatile memory, such as one or more disk storage devices (such as internal hard disks and removable disks), magneto-optical storage devices, optical disc storage devices, flash memory devices, semiconductor memory devices (such as erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM)), compact disc read-only memory (CD-ROM), digital versatile disc read-only memory (DVD-ROM) discs, or other non-volatile solid-state storage devices. The heart monitoring device 100 also includes a removable storage device 118. The removable storage device 118 includes a port and a corresponding removable storage medium. For example, the removable storage device 118 can be a Secure Digital (SD) port and a corresponding SD card, but the described invention is not limited thereto, but can also use any other type of removable storage device.

[0048] The heart monitoring device 100 may also include a display 120 and one or more other input / output devices 122 (which can enable user interaction with the heart monitoring device 100). For example, the display 120 can be a liquid crystal display (LCD) that displays information to the user. The other input / output devices 122 can include: input devices (such as touchscreens, keypads, buttons, etc.), through which the user can provide input to the heart monitoring device 100; input ports, such as USB ports, mini-USB ports, micro-USB ports, etc.; and output devices, such as speakers, headphone jacks, light-emitting diodes (LEDs), etc. The heart monitoring device 100 also includes a power source 126 (such as a rechargeable battery).

[0049] The cardiac monitoring device 100 may further include one or more network interfaces 124 for communicating with other devices via one or more networks. In accordance with an advantageous embodiment, the network interface 124 can include a cellular network interface for communicating via a cellular network such as a Global System for Mobile Communications (GSM) network, a Code Division Multiple Access (CDMA) network, or a Long Term Evolution (LTE) network. Such cellular networks can be 3G or 4G networks through which data can be transmitted. The network interface 124 can also include a Short Message Service (SMS) and / or Multimedia Messaging Service (MMS) network interface for transmitting and receiving text messages and / or multimedia messages. The network interface 124 may further include a Wireless Network Interface Controller (WNIC) for wireless communication via a data network such as a WIFI network. The network interface 124 may also include a network interface for short-range wireless networks such as Bluetooth.

[0050] The mobile cardiac monitoring device 100 is communicatively coupled to the ECG electrodes 128. In one embodiment, the ECG electrodes can be connected to the cardiac monitoring device 100 via a cable. For example, the ECG electrode 128 can be connected to a USB cable that is inserted into a USB port of the mobile device. It is to be understood that the described invention is not limited to a USB cable, but other types of cables can also be used. In another embodiment, the ECG electrode 128 can communicate wirelessly with the mobile cardiac monitoring device 100. For example, the ECG electrode 128 can communicate with the mobile cardiac monitoring device 100 via a Bluetooth connection. The ECG electrode 128 is placed on the body of the user or patient and transmits voltage-time measurements of a subset of the ECG leads to the mobile cardiac monitoring device 100. In accordance with an advantageous embodiment, voltage-time measurements of three ECG leads are received from the ECG electrode 128. In an exemplary implementation, ECG leads I, II, and V2 are measured by the ECG electrode 128. In another possible implementation, ECG leads I, aVF, and V2 can be measured by the ECG electrode 128. The ECG electrode 128 can include five electrodes for measuring three ECG leads, with one of the electrodes being a ground. In a possible embodiment, the ground can be included in one of the other electrodes, and a smaller total number of electrodes can be used.

[0051] Figure 2 Shows the placement of the ECG electrodes on the body of a user, in accordance with an embodiment of the described invention. As Figure 2As shown, five electrodes 202, 204, 206, 208, and 210 are placed on a user. Electrode 202 is placed on the left arm (LA), electrode 204 is placed on the right arm (RA), electrode 206 is placed on the left leg (LL), electrode 208 can be placed on the right leg (RL), and electrode 210 is placed at the V2 lead position, which is in the fourth intercostal space near the sternum. Electrodes 202, 204, and 206 can be placed at any position on their corresponding limbs, making their placement easy for the user. Electrode 208 is a ground and is typically placed on the right leg, making its placement easy for the user, but the position of the ground electrode is not limited to the right leg and can also be placed at other positions. Electrode 210 also corresponds to an anatomical position that is easy for the user to locate. If the electrodes are not placed directly on muscle (which may cause interference), the signal from the electrodes can be improved. In an exemplary alternative implementation, the ground can be included in the V2 electrode (210). In this case, electrode 208 is not needed and instead four electrodes can be used. In other possible implementations, the ground can also be in one of the other electrodes. Using Figure 2 the electrode placement, the ECG electrodes measure ECG leads I, II, and V2, which are members of the set of leads that make up the standard 12-lead ECG. Those skilled in the art will recognize that other electrodes placed on the body surface to record other sets of basic orthogonal leads can also be utilized. For example, the placement of V9 in the posterior thorax (behind V2) can replace V2 in the example described above to derive an n-lead ECG and construct a CEB.

[0052] Returning to Figure 1 , the ECG derivation module 108, the heart rate and rhythm module 110, the dynamic CEB calculation module 112, and the alert module 114 can be stored in the data storage device 106. Each of these modules includes computer program instructions for performing a specific set of operations when loaded into the memory 104 and executed by the processor 102. The data storage device 106 also includes a patient data storage device 116 for storing various patient data, including voltage-time measurements received from the ECG electrodes 128, the derived ECG data generated by the ECG derivation module 108, the heart rate and rhythm data generated by the heart rate and rhythm module 110, and the cardiac electrobiomarker (CEB) data generated by the dynamic CEB calculation module 112.

[0053] The standard ECG is measured by placing a series of electrodes on the patient's skin. The standard ECG recording includes 12-lead waveforms (designated as I, II, III, aVR, aVL, aVF, V1, V2, V3, V4, V5, and V6), which are arranged in a specific order that is interpreted by a physician using pattern recognition techniques. In a typical configuration, 10 electrodes are placed on the body torso to measure the potentials that define the standard 12 leads. In accordance with an embodiment of the described invention, the ECG derivation module 108 is capable of deriving a complete set of ECG leads from a subset of the ECG leads measured by the ECG electrodes 128. In some such embodiments of the described invention, the ECG derivation module 108 is capable of deriving a complete n-lead (e.g., 12-lead) ECG of the patient from 3 measured leads received from the ECG electrodes 128. The ECG derivation module 108 is capable of deriving the complete n-lead ECG from the 3 measured leads by applying a stored general transformation matrix (generated from a set of training ECG data using abstract factor analysis and simplex optimization algorithms). This method for deriving an n-lead ECG is described in more detail in U.S. Patent No. 6,901,285, which is incorporated herein by reference in its entirety.

[0054] Figure 3 An embodiment of the described invention for deriving an n-lead ECG is shown. Figure 3 The method steps can be performed by the ECG derivation module 108 to derive a complete n-lead ECG from the voltage-time measurements of 3 ECG leads received from the ECG electrodes 128. Referring to Figure 3 , at step 302, digitized voltage-time measurements of ECG leads I, II, and V2 are received from the ECG electrodes 128. Lead I is the voltage between the left arm (LA) electrode and the right arm (RA) electrode: Lead I = LA - RA. Lead II is the voltage between the left leg (LL) electrode and the RA electrode: Lead II = LL - RA. Lead V2 is the voltage between the positive electrode at the V2 electrode and the negative electrode of a composite electrode known as the central terminal of Wilson, which is generated by averaging the measurements from the electrodes RA, LA, and LL to give the average potential across the body: Lead V2 = V2 – 1 / 3(RA + LA + LL).

[0055] At step 304, the aVF ECG lead is calculated from the measured ECG leads I and II. The aVF (augmented vector foot) lead can be calculated from the known geometry of leads I and II. The aVF lead has a positive electrode on the left leg, and the negative electrode is a combination of the right arm and left arm electrodes. Due to the built-in redundancy in the standard 12-lead ECG, the measurement of any 2 of the first 6 leads can be used to calculate the other 4 leads according to the following geometry-based formulas:

[0056] Lead III = Lead II – Lead I

[0057] Lead aVR = -0.87 × ((Lead I + Lead II) / 2)

[0058] Lead aVL = 0.87 × ((Lead I – Lead III) / 2)

[0059] Lead aVF = 0.87 × ((Lead II + Lead III) / 2).

[0060] Accordingly, the aVF lead can be calculated from Lead I and Lead II as: Lead aVF = [((2 × Lead II) – Lead I) / 2] × 0.87. This results in three orthogonal leads I, aVF, and V2. According to an alternative embodiment, the above equations can also be calculated without the 0.87 coefficient, such that the following equations are used for Lead aVR, Lead aVL, and Lead aVF: Lead aVR = -((Lead I + Lead II) / 2); Lead aVL = ((Lead I – Lead III) / 2); and Lead aVF = ((Lead II + Lead III) / 2). Although Figure 3 the method obtains voltage-time measurements of Leads I, II, and V2 and then calculates Lead aVF from Leads I and II, in an alternative embodiment, the voltage-time measurements of Leads I, aVF, and V2 can be obtained directly from the ECG electrodes. For example, Lead aVF can be obtained as: Lead aVF = LL – 1 / 2(RA + LA).

[0061] In step 306, the n-lead ECG is derived from Leads I, aVF, and V2 using a general transformation matrix. The general transformation matrix is derived from a training set of ECG data and is stored in the data storage device 106 as part of the ECG derivation module 108. Without limitation, an example of a set of leads that can be derived from 3 leads (I, aVF, and V2) is:

[0062] 12 leads: I, II, III, aVR, aVL, aVF, V1, V2, V3, V4, V5, V6;

[0063] 15 leads: I, II, III, aVR, aVL, aVF, V1, V2, V3, V4, V5, V6, X, Y, Z;

[0064] 15 leads: I, II, III, aVR, aVL, aVF, V1, V2, V3, V4, V5, V6, V7, V8, V9;

[0065] 16 leads: I, II, III, aVR, aVL, aVF, V1, V2, V3, V4, V5, V6, V3R, V4R, V5R, V6R;

[0066] 18 leads: I, II, III, aVR, aVL, aVF, V1, V2, V3, V4, V5, V6, V7, V8, V9, X, Y, Z;

[0067] 22 leads: I, II, III, aVR, aVL, aVF, V1, V2, V3, V4, V5, V6, V7, V8, V9, V3R, V4R, V5R, V6R, X, Y, Z.

[0068] The general transformation matrix is specific to the number of leads in the resulting n - lead ECG. The general transformation matrix is generated from a training set of the ECG voltage - time data arrays. Specifically, abstract factor analysis ("AFA") techniques can be applied to each of the ECG voltage - time arrays in the training set in order to minimize the error in the measured arrays. Simplex optimization techniques ("SOP") are then applied to the training set in order to arrive at a general transformation matrix that is applicable to all patients and is time - independent. In addition to being time - independent, the general transformation matrix is also capable of being independent of other characteristics such as gender, body type, etc. However, it is also possible that more specific transformation matrices can be used for specific characteristics (such as gender, body type, gender, etc.) based on the training data used to arrive at the general transformation matrix. The general transformation matrix is an N×3 matrix that is applied to a subset of 3 leads to generate the complete n - lead ECG. Specifically, the N×3 general transformation matrix is multiplied by a vector that includes the 3 leads {I, aVF, V2} for a particular time to produce the complete n - lead ECG. Those skilled in the art will understand that {I, aVF, V2} is an approximately basis - orthogonal lead set that is necessary for constructing the general transformation matrix. Other such basis - orthogonal lead sets can be used to perform this step, as will be recognized by those skilled in the art. For example, other exemplary basis - orthogonal lead sets include {I, aVF, V9}, {V6R, aVF, V2}, and {V6R, aVF, V9}, but the present invention is not limited thereto.

[0069] Return to Figure 1, the heart rate and rhythm module 110 identifies the heart rhythm and calculates the user's heart rate from at least one of the measured ECG leads. An ECG is typically presented as a graph plotting the electrical activity of the heart on the vertical axis against time on the horizontal axis. Standard ECG paper moves at 25 mm per second during real-time recording. This means that when viewing a printed ECG, a distance of 25 mm along the horizontal axis represents 1 second. The ECG paper is marked with a grid of small and large squares. Each small square represents a time of 40 milliseconds (ms) along the horizontal axis, and each larger square contains 5 small squares and thus represents 200 ms. The standard paper speed and square markings allow for easy measurement of the cardiac timing intervals. This enables the calculation of the heart rate and the identification of abnormal electrical conductance within the heart. On an ECG, the amplitude or voltage of the recorded electrical signal is expressed in the vertical dimension and is measured in millivolts (mV). On standard ECG paper, 1 mV is represented by a 10 mm deflection.

[0070] Figure 4 Figure 4 shows a typical cardiac electrical signal as measured by an ECG. Since the first structure to be depolarized during normal sinus rhythm is the right atrium, followed immediately by the left atrium, the first electrical signal on a normal ECG originates from the atria and is called the P wave. Although there is usually only one P wave in most leads of an ECG, the P wave is actually the sum of electrical signals from both atria (which are usually superimposed). There is a short physiological delay (which is responsible for the PR interval: a short period where no electrical activity is seen on the ECG, represented by a horizontal or "isoelectric" straight line) because the atrioventricular (AV) node slows down the electrical depolarization before it proceeds to the ventricles. The depolarization of the ventricles produces the QRS complex, which is usually the largest part of the ECG signal. The Q wave is the first initial downward, or negative deflection if the first initial downward is negative, the R wave is the next upward deflection, and the S wave is the next downward deflection. The electrical signals reflecting the repolarization of the myocardium are shown as the ST segment and the T wave. The ST segment is usually isoelectric, and the T wave in most leads has a vertical deflection of variable amplitude and duration. The T wave may be followed by an additional low-amplitude wave (called the U wave). This later repolarization usually has the same polarity as the previous T wave. The PR interval is measured from the start of the P wave to the first deflection of the QRS complex and has a normal range of 120 - 200 ms (3 - 5 small squares on the ECG paper). The QRS duration is measured from the first deflection of the QRS complex to the end of the QRS complex at the isoelectric line and has a normal range of up to 120 ms (3 small squares on the ECG paper). The QT interval is measured from the first deflection of the QRS complex to the end of the T wave at the isoelectric line and has a normal range of up to 440 ms, but this varies with the heart rate and may be slightly longer in women.

[0071] The heart rate and rhythm module 110 can calculate the patient's heart rate by determining the amount of time between each QRS complex in one or more ECG leads. The time per second in the ECG signal can be estimated by 25 mm (5 large squares) along the horizontal axis. Accordingly, the number of large squares between each QRS complex in the ECG lead provides an approximate amount of time between each QRS complex, which can be used to estimate the heart rate. For example, if the number of large squares between each QRS complex is 5, the heart rate is 60 beats per minute; if the number of large squares between each QRS complex is 3, the heart rate is 100 beats per minute; if the number of large squares between each QRS complex is 2, the heart rate is 150 beats per minute. It is to be understood that the standard paper rate and square markings can be scaled for the display of the ECG signal on the display 120, and the heart rate can be estimated similarly. The heart rate and rhythm module 110 can also evaluate the acquired and / or derived ECG signal to monitor the heart rhythm to help identify whether the heart rhythm is regular or irregular.

[0072] The dynamic CEB calculation module 112 calculates the CEB from the derived ECG. CEB is an electro-biological marker that quantifies the dipole energy content in the cardiac electric field. The more dipole energy content present in the cardiac electric field, the more normal the patient's condition, while the more multipole energy content present in the cardiac electric field, the more abnormal the patient's condition. CEB can be used as a "point-of-care" diagnostic test to detect the presence or absence of acute myocardial ischemic injury (AMII) (including acute myocardial infarction (AMI)). CEB can also be used to monitor patients initially not diagnosed with AMII / AMI to monitor and detect the onset and / or progression of AMII / AMI in real time. The electric field of the heart begins at the cellular level, and in the case of AMII / AMI, there is a minimal multipole component to the electric field. CEB measures the dipole electrical activity in the electric field of the heart.

[0073] According to an embodiment of the described invention, the CEB can be calculated from the derived ECG by calculating the third eigenvalue of the derived ECG voltage-time data. Specifically, abstract factor analysis (AFA) can be used to calculate the eigenvectors of the derived ECG voltage-time data. Let D denote the data matrix array of the derived ECG voltage-time data. The covariance matrix Z can then be constructed by multiplying D by its transpose matrix as follows: Z = D T D. The covariance matrix Z is then diagonalized by finding the matrix Q such that Q -1 ZQ = λ j δ jk , where d jk is the Kronecker delta such that djk = 0 (if j ≠ k ), and d jk = 1 (if j = k ), and λ j is an eigenvalue of the set of equations Zq j = λ j q j where q j is the j-th column of the Q eigenvector. As calculated, the third eigenvalue ( λ 3 ) is used as the CEB. The inventors have determined that the third eigenvalue, which provides a measure of the dipole activity of the cardiac electric field, can be used as the CEB, which indicates acute myocardial ischemic injury. Generally, the more multipolar (less dipolar) forces in the cardiac electric field, the greater the likelihood of AMII / AMI. The CEB has a value that quantifies the multipolar forces (suggestive of AMI) in the cardiac electric field. For example, a CEB value less than 66 can indicate a normal condition, a CEB value between 66 and 94 can be considered in an indeterminate region, and a CEB value greater than 94 can indicate an abnormal condition. It is to be understood that the present invention is not limited to these specific cut-off values, and the cut-off values can be changed based on user operability and changes in more specific general transformation matrices.

[0074] In an advantageous embodiment of the described invention, the dynamic CEB calculation module 112 calculates the dynamic CEB by calculating the corresponding CEB value from the derived ECG for each heartbeat. In this case, factor analysis in the abstract is applied to the derived ECG voltage-time data for each heartbeat to calculate the third eigenvalue of the derived ECG voltage-time data for each heart, thereby generating the corresponding CEB value for each heartbeat. The dynamic CEB data can be displayed by the display 120 as a graph of CEB versus time. In another possible embodiment, a plurality of heartbeats in the derived ECG over a predetermined time interval (e.g., 10 seconds) are averaged to a median beat, and a static CEB is calculated based on the median beat ECG data for that time interval. In generating the median beat, beats of the same shape are combined into cycles that are precisely represented. The noise is greatly reduced by this process. Successive CEBs over a predetermined time interval can cause the display of the dynamic CEB in this instance.

[0075] In another possible embodiment, a fractal CEB can be calculated to replace or in addition to the eigenvalue CEB. The method described in U.S. Patent No. 6,920,349 (incorporated herein by reference in its entirety) can be used to calculate the fractal CEB. In this case, a spatial curve can be defined from the lead values of at least three leads of the resulting ECG. The fractal exponent of the spatial curve is calculated as a function of time. As an example, the rate of change of the fractal exponent over time can be calculated as the CEB. A negative rate of change indicates normal cardiac activity, while a positive rate of change indicates pathological activity. In a possible implementation, the dynamic CEB calculation module 112 can calculate the eigenvalue CEB and the fractal CEB for each heartbeat, and the alarm module 114 can use the combination of the eigenvalue CEB and the fractal CEB to determine whether an alarm condition has been triggered. Other fractal analyses of the spatial curve can also be constructed. A series of multiple CEBs can be calculated and displayed, and / or transmitted to a device associated with a physician to assist the physician in understanding the onset and / or progression of AMII / AMI.

[0076] The alert module 114 monitors the CEB values calculated by the dynamic CEB calculation module 112 and controls the mobile cardiac monitoring device 110 to send an alert when a certain trigger condition is detected. In a possible embodiment, the alert module 114 can monitor the dynamic CEB values calculated for each heartbeat and determine whether the CEB value of each heartbeat is in an abnormal region. For example, for the eigenvalue CEB, a CEB value greater than 94 can be considered to be in the abnormal region. If, within a predetermined time interval, the programmable percentage of heartbeats in the abnormal region is greater than a threshold, the alert module 114 determines that a trigger condition has been detected and transmits an alert message via the network interface(s) 124. For example, the alert message can be a text message sent to a predetermined remote device (such as a device associated with the patient's physician). The text message can include the derived ECG data and / or the measured ECG leads, the estimated heart rate data, and the CEB data for a certain period of time before the detection of the trigger condition. Similarly, the alert message can be an email message sent to a predetermined email address, and the email message can include the patient's derived ECG data and / or the measured ECG leads, the heart rate data, and the CEB data. The alert module 114 can also control the mobile cardiac monitoring device 100 to place a telephone call to a telephone number associated with a predetermined remote device (such as the physician's telephone) and play a predetermined voice alert message. The alert module 114 can also control the mobile cardiac monitoring device 100 to automatically contact the emergency response system. For example, the alert module can control the mobile cardiac monitoring device 100 to automatically call 911 in response to the detection of a trigger condition. In a possible embodiment, data can be downloaded to a reader device (such as the Vetraplex ECG system), which can perform additional calculations and display additional information. For example, such a device can derive a 15- or 22-lead ECG from the measured ECG leads and display the derived 15- or 22-lead ECG.

[0077] Figure 5 A method of cardiac monitoring using a mobile cardiac monitoring device, showing an embodiment of the described invention. Figure 5 The method can be performed by Figure 1 the mobile cardiac monitoring device 100. Figure 5 The method steps can be repeated to provide real-time cardiac monitoring for a patient. In a demonstration implementation, the mobile cardiac monitoring device 100 can be provided to a patient who is not under the direct supervision of a doctor (such as a patient who exhibits chest pain but is sent home from the emergency room) and can perform Figure 5 the method to provide real-time remote cardiac monitoring for the patient. In another demonstration implementation, Figure 5 the method can be performed for real-time point-of-care for patients in a hospital, a doctor's office, etc.

[0078] Refer to Figure 5, at step 502, digitized voltage-time measurements are received for three ECG leads. For example, voltage-time measurements for leads I, II, and V2 or for leads I, aVF, and V2 can be received from ECG electrode 128. At step 504, a complete 12-lead ECG is derived from the voltage-time measurements of the three ECG leads. As described above, the ECG derivation module can use a pre-stored general transformation matrix to derive the 12-lead ECG. Although Figure 5 's method derives a 12-lead ECG, the described invention is not limited thereto, but can similarly derive any other n-lead ECG. For example, the mobile cardiac monitoring device can derive a complete 15-lead or 22-lead ECG. At step 506, the heart rate of the patient is calculated from the received voltage-time measurements of at least one of the ECG leads, and the heart rhythm is monitored. At step 508, the dynamic CEB is calculated from the derived 12-lead ECG. The dynamic CEB can be constructed by calculating the CEB value for each heartbeat. The CEB value for each heartbeat can be constructed by calculating the third eigenvalue of the derived 12-lead ECG voltage-time data corresponding to each heartbeat. It is also possible that other eigenvalue analyses can be performed.

[0079] Figure 5 's method then proceeds to three possible steps (510, 512, and 514). According to various embodiments, the mobile cardiac monitoring device 100 can perform any one of these steps, all of these steps, or any combination of these steps. At step 510, the derived ECG data, heart rate data, heart rhythm data, and CEB data of the patient are stored. Such patient data can be stored in the patient data storage device 116 of the data storage device 106 and / or on the removable storage device 118. In a possible implementation, the mobile cardiac monitoring device 100 can be used to monitor a patient for a specific period of time (e.g., 1 or 2 days), and the patient data obtained during that period is stored on the removable storage device 118. A doctor can remove the removable storage device 118 and load the patient data from the removable storage device into the doctor's computer (or another device) to observe the patient data.

[0080] In step 512, the derived ECG data, heart rate data, heart rhythm data, and ECB data of the patient can be displayed on the display 120 of the mobile cardiac monitoring device 100. Patient data can be displayed in real time as it is acquired and calculated. The derived ECG data can be displayed by displaying the ECG signal over time for each of the leads of the derived 12-lead ECG. It is also possible to display the ECG data by displaying a 3D spatial ECG loop generated by plotting three measured leads (I, aVF, and V2) of the derived 12-lead ECG or any other three orthogonal leads against each other in 3D space. It is also possible for the mobile cardiac monitoring device to display an ECG vector loop from the complete 15-lead and / or 22-lead ECG derived by the mobile cardiac monitoring device. The heart rate can be displayed as a numerical value (which is updated as needed). Dynamic CEB data (such as CEB values calculated for each heartbeat or for a predetermined interval of heartbeats) can be displayed as a graph of CEB over time. Dynamic CEB data can be displayed in real time as it is calculated. It is also possible to display dynamic or static CEB data as a numerical value (which is updated as it changes). In a possible embodiment, CEB data can be color-coded, for example, using different colors for CEB values corresponding to normal regions, indeterminate regions, and abnormal regions.

[0081] In step 514, the derived ECG data, heart rate data, heart rhythm data, and CEB data of the patient are transmitted to a remote device. For example, the patient data can be transmitted to a computer or another device associated with a doctor or a remote monitoring system. For example, the data can be transmitted to a reader device that can calculate the patient's 15- or 22-lead ECG. In another possible embodiment, the complete 15- and / or 22-lead ECG can be derived by the cardiac monitoring device and transmitted to the remote device. Patient data can be transmitted in real time as it is acquired and calculated. This allows the doctor to monitor the patient data in real time even when the patient is located remotely. In another possible implementation, the patient data can be transmitted at programmable time intervals. In another possible implementation, the patient can manually trigger the mobile cardiac monitoring device 100 to transmit the data. For example, the mobile cardiac monitoring device can be equipped with an event button that the patient / user can select to manually trigger the transmission of the patient data. The patient data can be transmitted via any type of data network (such as a cellular network, WIFI, text or multimedia messaging, Bluetooth, etc.) using one or more network interfaces 124. In a possible implementation, the patient data can be transmitted to a monitoring service that can then monitor the patient data to detect emergencies in place of or in addition to the alarm module 114 in the mobile cardiac monitoring device 100.

[0082] Figure 6A method of cardiac monitoring and alert notification using a mobile cardiac monitoring device, showing an embodiment of the described invention. Figure 6 The method can be performed by Figure 1 the mobile cardiac monitoring device 100. Figure 6 The method steps can be repeated to provide real-time cardiac monitoring for the patient. In a demonstration implementation, the mobile cardiac monitoring device 100 can be provided to a patient not under the direct supervision of a doctor (such as a patient showing chest pain but sent home from the emergency room), and can perform Figure 6 the method to provide real-time remote cardiac monitoring for the patient. In another demonstration implementation, Figure 6 the method can be performed for real-time point-of-care of patients in a hospital, a doctor's office, etc.

[0083] Referring to Figure 6 , at step 602, digitized voltage-time measurements are received for three orthogonal ECG leads. For example, voltage-time measurements of leads I, II, and V2 or voltage-time measurements of leads I, aVF, and V2 can be received from the ECG electrodes 128. At step 604, a complete 12-lead ECG is derived from the voltage-time measurements of the three ECG leads. As described above, the ECG derivation module can use a pre-stored general transformation matrix to derive the 12-lead ECG. Although Figure 5 the method derives a 12-lead ECG, the described invention is not limited thereto, but can similarly derive any other n-lead ECG. At step 606, the heart rate of the patient is calculated from the received voltage-time measurements of at least one of the ECG leads, and the heart rhythm is monitored. At step 608, the dynamic CEB is calculated from the derived 12-lead ECG. The dynamic CEB can be constructed by calculating the CEB value for each heartbeat or for a specific interval of heartbeats. The CEB value for each heartbeat can be constructed by calculating the eigenvalue of the derived 12-lead ECG voltage-time data corresponding to each heartbeat.

[0084] In step 610, it is determined whether a trigger condition is detected. To determine whether a trigger condition is detected, for each heartbeat (or the interval between heartbeats), it is determined whether the CEB associated with that heartbeat is in an abnormal region. For example, for the eigenvalue CEB, a CEB value greater than 94 may be considered to be in an abnormal region. When the programmable percentage of heartbeats having CEB values in the abnormal region within a predetermined time interval is greater than a threshold, the trigger condition can be detected. That is, when P > τ, the trigger condition is detected, where P is the percentage of heartbeats having CEB values in the abnormal region within a time interval t (e.g., 1 minute), and τ is the percentage threshold (e.g., 90%). It is also possible to detect the trigger condition based on the average CEB value for a certain time interval, based on the static eigenvalue CEB calculated for the median heartbeat within a certain time interval, based on the fractal CEB, or based on a combination of the fractal and eigenvalue CEB or other combinations of CEBs. If the trigger condition is not detected, the method returns to step 602, and continues to monitor the patient by repeating steps 602, 604, 606, and 608. If the trigger condition is detected, the method proceeds to step 610.

[0085] In step 612, when the trigger condition is detected, an alert is transmitted to a predetermined remote device. The alert can be a text message sent to the predetermined remote device (such as a device associated with the patient's doctor) via a text message, email, phone call, or any other type of message. The alert message (such as a text message or email) can include the derived ECG data, the calculated heart rate data, the heart rhythm information, and the CEB data within a certain time period before the detection of the trigger condition. In addition to the alert message including the patient data, a phone alert message with a predetermined voice message can also be sent to a predetermined phone number. The method returns to step 602, and continues to monitor the patient by repeating steps 602, 604, 606, and 608.

[0086] As described above, the mobile cardiac monitoring device can transmit data to a remote device. Figure 7 The communication between the mobile cardiac monitoring device 700 and the reader device 710 according to an embodiment of the present invention is shown. The mobile cardiac monitoring device 700 can communicate with Figure 1The mobile cardiac monitoring device 100 is similarly implemented. The reader device 710 is a device capable of performing the following operations: being able to receive data from the mobile cardiac monitoring device 700, deriving additional information from the data, and displaying the information to a physician. For example, the reader device may be a Vetraplex ECG system located in a physician's office or hospital. According to a possible implementation, the mobile cardiac monitoring device 700 may send the acquired voltage-time measurements of a subset of the ECG leads to the reader device 710. In other possible implementations, additional data (such as the calculated CEB value, heart rate data, heart rhythm data, and / or the derived 12-lead ECG data) may also be sent from the mobile cardiac monitoring device 700 to the reader device 710. The mobile cardiac monitoring device 700 may use any type of data transmission protocol to directly send data to the reader device 710. It is also possible that the mobile cardiac monitoring device may upload the data to a data network or "cloud" 702, which can then transmit the data to the reader device 710 and / or other remote devices associated with the physician. The reader device 710 may derive the complete 15- or 22-lead ECG of the patient based on the acquired subset of the ECG leads or the derived 12-lead ECG data received from the mobile cardiac monitoring device 700, and display the derived 15- or 22-lead ECG to the physician. The reader device may also calculate the static CEB value based on the derived n-lead ECG and display the CEB value. In a demonstration implementation, the mobile cardiac monitoring device 700 is capable of transmitting data to the reader device 710 (or to the cloud 702) at a predetermined (programmable) time interval. It is also possible that the mobile cardiac monitoring device 700 is capable of transmitting data to the reader device 710 (or to the cloud 702) in response to the detection of an alarm condition in the mobile cardiac monitoring device or in response to a manual trigger (such as the selection of an event button) input by the patient in the mobile cardiac monitoring device 700. It is also possible that the mobile cardiac monitoring device 700 is capable of transmitting data to the reader device 710 (or to the cloud 702) in response to a request for the data received in the mobile cardiac monitoring device 700.

[0087] Figure 8 A system for cardiac monitoring of a patient according to an embodiment of the present invention is shown. As Figure 8 shown, the system includes a central monitoring system 800 and a plurality of mobile cardiac monitoring devices 802, 804, 806, 808, 810, and 812. The mobile cardiac monitoring devices 802, 804, 806, 808, 810, and 812 are capable of being Figure 1 implemented similarly to the mobile cardiac monitoring device 100. It is also possible that, Figure 8The mobile cardiac monitoring devices 802, 804, 806, 808, 810, and 812 can be implemented without the alert module 114 or without any one of the ECG derivation module 108, the heart rate estimation module 110, the dynamic CEB calculation module 112, and the alert module 114. The mobile cardiac monitoring devices 802, 804, 806, 808, 810, and 812 are each associated with a respective patient and transmit the respective patient data to the central monitoring system 800. The central monitoring system 800 monitors the patient data of each patient associated with the mobile cardiac monitoring devices 802, 804, 806, 808, 810, and 812. The mobile cardiac monitoring devices 802, 804, 806, 808, 810, and 812 can transmit the patient data via any type of data network (such as WIFI, Bluetooth, etc.). In one example, Figure 8 The system can be implemented in a hospital, and each patient can be provided with one of the mobile cardiac monitoring devices 802, 804, 806, 808, 810, and 812. The central monitoring system 800 can then be used to simultaneously monitor all patients or all patients on a floor or section of the hospital.

[0088] In one possible implementation, each of the mobile cardiac monitoring devices 802, 804, 806, 808, 810, and 812 acquires 3-lead ECG voltage-time measurements of the respective patient and transmits the 3-lead ECG voltage-time measurements to the central monitoring system 800. The central monitoring system then derives a complete n-lead (e.g., 12-lead) ECG for each patient, estimates the heart rate of each patient based on the derived ECG, and dynamically calculates the CEG of each patient based on the derived ECG. In an exemplary implementation, the central monitoring system 800 can derive a 15- or 22-lead ECG for each patient. The central monitoring system 800 also monitors the patient CEB data calculated for each patient to detect a trigger condition. In an advantageous implementation, the central monitoring system 800 performs ECG derivation, heart rate calculation, arrhythmia interpretation, CEB calculation, and trigger condition detection in a manner similar to that described above for Figure 1 the mobile cardiac monitoring device 100, but for each of multiple patients. In another possible implementation, the central monitoring system 800 can be associated with one or more reader devices (such as Figure 7communicates with the reader device 710), the central monitoring system 800 can derive a 15- or 22-lead ECG and can calculate the CEB value for each patient. If a trigger is detected for any patient, the central monitoring system 800 provides an alarm. For example, the central monitoring system can provide an audible alarm (such as a siren) and a visual alarm (such as a flashing light) to indicate to the doctor which patient is associated with the detected trigger condition. The central monitoring system 800 can also send an alarm message (such as a text message, a phone call, etc.) to a device associated with the doctor. In another possible implementation, each of the mobile cardiac monitoring devices 802, 804, 806, 808, 810, and 812 can acquire 3-lead ECG voltage-time measurements, derive a complete n-lead ECG, estimate the heart rate, and calculate the CEB for the corresponding patient, and then transmit the derived ECG, the estimated heart rate, and the calculated CEB for the corresponding patient to the central monitoring system 800 in real time. The central monitoring system 800 then monitors the CEB of each patient to detect whether a trigger condition has occurred and generates an alarm notification for the patient when the trigger condition is detected.

[0089] The central monitoring system 800 can be implemented on one or more computers using well-known computer processors, memory units, storage devices, computer software, and other components. The processor controls the overall operation of the central monitoring system 800 by executing computer program instructions that define the overall operation. The computer program instructions can be stored in a storage device (such as a disk) and loaded into the memory when it is desired to execute the computer program instructions. For example, the computer program instructions for performing Figure 3 , Figure 5 and Figure 6 the method steps can be stored in the memory and / or storage device and controlled by the processor executing the computer program instructions. The central monitoring system 800 includes one or more network interfaces for communicating with other devices (such as the mobile cardiac monitoring devices 802, 804, 806, 808, 810, and 812) via a network. The central monitoring system 800 also includes one or more displays for displaying patient data of various patients and for displaying an alarm notification when a trigger condition is detected for a patient. The central monitoring system 800 also includes other input / output devices that can enable user interaction with the central monitoring system 800 (such as a keyboard, a mouse, a speaker, a button, etc.).

[0090] The foregoing specific embodiments are to be understood as illustrative and exemplary in every aspect and not restrictive, and the scope of the invention disclosed herein is not to be determined from the specific embodiments, but rather from the claims as interpreted in accordance with the full breadth permitted by patent law. It is to be understood that the embodiments shown and described herein are only illustrative of the principles of the described invention, and that various modifications may be made by those skilled in the art without departing from the scope and spirit of the invention. Without departing from the scope and spirit of the invention, those skilled in the art may effect various other combinations of features.

Claims

1. A method for obtaining dynamic cardiac electrobiomarkers, comprising: Storing a general transformation matrix, which is generated by the following steps: applying an abstract factor analysis technique to each electrocardiogram voltage-time data array in a training set; and then applying a simplex optimization technique to the training set; Receiving voltage-time measurements of a subset of the user's electrocardiogram leads at a mobile cardiac monitoring device, the mobile cardiac monitoring device receiving voltage-time measurements of a basic set of three orthogonal electrocardiogram leads from four or five electrodes in communication with the mobile cardiac monitoring device; Deriving a complete set of the user's electrocardiogram leads from the subset of electrocardiogram leads by: applying the stored general transformation matrix to the basic set of three orthogonal electrocardiogram leads to generate an n-lead electrocardiogram, thereby deriving a complete set of electrocardiogram leads of the n-lead electrocardiogram from the voltage-time measurements of the basic set of three orthogonal electrocardiogram leads; Calculating a heart rate based on the voltage-time measurements of at least one lead in the subset of electrocardiogram leads and monitoring the user's heart rhythm; Calculating a cardiac electrobiomarker for each of a plurality of heartbeats from the derived complete set of electrocardiogram leads, calculating a dynamic cardiac electrobiomarker from the derived complete set of electrocardiogram leads, wherein the dynamic cardiac electrobiomarker quantifies dipole and multipole energy content; wherein Calculating a dynamic cardiac electrobiomarker for each of a plurality of heartbeats from the derived complete set of electrocardiogram leads includes, for each of the plurality of heartbeats: using abstract factor analysis to calculate a set of eigenvectors of the voltage-time data of the derived complete set of electrocardiogram leads; And calculating first, second, and third eigenvalues from the set of eigenvectors, wherein the third eigenvalue is used as a cardiac electrobiomarker for each of the plurality of heartbeats.

2. The method according to claim 1, wherein, Deriving the complete set of electrocardiogram leads, calculating the heart rate, and calculating the cardiac electrobiomarker are all performed by the mobile cardiac monitoring device.

3. The method according to claim 1, wherein, Receiving voltage-time measurements of the basic set of three orthogonal electrocardiogram leads includes: (a) Receiving voltage-time measurements of the basic set of three orthogonal electrocardiogram leads from four or five electrodes in communication with the mobile cardiac monitoring device, wherein the ground is located within one of the electrodes recording the basic set of three orthogonal electrocardiogram leads; or (b) Receiving voltage-time measurements of the I, aVF, and V2 electrocardiogram leads; or (c) Receiving voltage-time measurements of the I, II, and V2 electrocardiogram leads.

4. The method according to claim 1, wherein (a) The n-lead electrocardiogram is a 12-lead electrocardiogram; or (b) The n-lead electrocardiogram is a 15-lead or 22-lead electrocardiogram.

5. The method according to claim 1, further comprising: Displaying a graph of the change of the dynamic cardiac electrobiomarker over time on a display of the mobile cardiac monitoring device.

6. The method according to claim 1, further comprising: (a) Store the obtained complete set of electrocardiogram leads, the calculated heart rate, and the calculated dynamic cardiac electrobiomarkers on the removable storage device of the mobile cardiac monitoring device; and / or (b) Display on the display of the mobile cardiac monitoring device at least one of the calculated dynamic cardiac electrobiomarkers, the calculated heart rate, the obtained complete set of electrocardiogram leads, or the voltage-time measurements of the subset of electrocardiogram leads; and / or (c) Transmit in real time the obtained complete set of electrocardiogram leads, the calculated heart rate, and the calculated dynamic cardiac electrobiomarkers to a remote device.

7. The method according to claim 1, wherein calculating the dynamic cardiac electrobiomarkers from the obtained complete set of electrocardiogram leads comprises: Calculating the dynamic cardiac electrobiomarkers from the 12 leads of the n-lead electrocardiogram.

8. The method according to claim 1, wherein receiving the voltage-time measurements of the subset of electrocardiogram leads of the user by the mobile cardiac monitoring device comprises: Receiving the voltage-time measurements of the subset of electrocardiogram leads from a set of electrocardiogram electrodes via a wireless communication protocol.

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